Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5331-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5331-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 Aug 2021
Model description paper |
| 26 Aug 2021
| 26 Aug 2021
The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies
Antoine Berchet
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Espen Sollum
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Rona L. Thompson
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Isabelle Pison
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Joël Thanwerdas
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Tuula Aalto
Finnish Meteorological Institute (FMI), Helsinki, Finland
Adrien Berchet
Institut Pprime (UPR 3346, CNRS-Université de Poitiers-ENSMA), Bat H2, 11 Boulevard Marie et Pierre Curie, TSA 51124, 86073, Poitiers CEDEX 9, France
Peter Bergamaschi
European Commission Joint Research Centre, Ispra, Varese, Italy
Dominik Brunner
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Richard Engelen
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Audrey Fortems-Cheiney
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Christoph Gerbig
Max Planck Institute for Biogeochemistry, Jena, Germany
Christine D. Groot Zwaaftink
Norwegian Institute for Air Research (NILU), Kjeller, Norway
Jean-Matthieu Haussaire
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Stephan Henne
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Sander Houweling
Vrije Universiteit Amsterdam, Department of Earth Sciences, Earth and Climate Cluster, Amsterdam, the Netherlands
Ute Karstens
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
Werner L. Kutsch
Integrated Carbon Observation System (ICOS-ERIC), Helsinki, Finland
Ingrid T. Luijkx
Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, the Netherlands
Guillaume Monteil
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
Paul I. Palmer
School of GeoSciences, The University of Edinburgh, Edinburgh, EH9 3FF, UK
Jacob C. A. van Peet
Vrije Universiteit Amsterdam, Department of Earth Sciences, Earth and Climate Cluster, Amsterdam, the Netherlands
Wouter Peters
Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, the Netherlands
Centre for Isotope Research, University of Groningen, Groningen, the Netherlands
Philippe Peylin
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Elise Potier
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Christian Rödenbeck
Max Planck Institute for Biogeochemistry, Jena, Germany
Marielle Saunois
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Marko Scholze
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
Aki Tsuruta
Finnish Meteorological Institute (FMI), Helsinki, Finland
Yuanhong Zhao
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
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- Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE D. Savas et al. 10.3390/atmos14010154
- Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions K. Tibrewal et al. 10.1038/s43247-023-01190-w
- CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes A. Tsuruta et al. 10.3390/rs15061620
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- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. 10.3390/atmos15010051
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- Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling M. Remaud et al. 10.5194/acp-22-2525-2022
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- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2018 A. Petrescu et al. 10.5194/essd-13-2363-2021
20 citations as recorded by crossref.
- The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019 A. Petrescu et al. 10.5194/essd-15-1197-2023
- The consolidated European synthesis of CO2emissions and removals for the European Union and United Kingdom: 1990–2020 M. McGrath et al. 10.5194/essd-15-4295-2023
- Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE D. Savas et al. 10.3390/atmos14010154
- Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions K. Tibrewal et al. 10.1038/s43247-023-01190-w
- CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes A. Tsuruta et al. 10.3390/rs15061620
- Comparison of observation- and inventory-based methane emissions for eight large global emitters A. Petrescu et al. 10.5194/essd-16-4325-2024
- Accounting for meteorological biases in simulated plumes using smarter metrics P. Vanderbecken et al. 10.5194/amt-16-1745-2023
- Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale E. Potier et al. 10.5194/amt-15-5261-2022
- NOx emissions in France in 2019–2021 as estimated by the high-spatial-resolution assimilation of TROPOMI NO2 observations R. Plauchu et al. 10.5194/acp-24-8139-2024
- Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS J. Thanwerdas et al. 10.5194/gmd-15-4831-2022
- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. 10.3390/atmos15010051
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions M. Vojta et al. 10.5194/gmd-15-8295-2022
- Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling M. Remaud et al. 10.5194/acp-22-2525-2022
- CO anthropogenic emissions in Europe from 2011 to 2021: insights from Measurement of Pollution in the Troposphere (MOPITT) satellite data A. Fortems-Cheiney et al. 10.5194/acp-24-4635-2024
- Toward High‐Resolution Global Atmospheric Inverse Modeling Using Graphics Accelerators F. Chevallier et al. 10.1029/2022GL102135
- Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019 S. Wittig et al. 10.5194/acp-23-6457-2023
- Surface networks in the Arctic may miss a future methane bomb S. Wittig et al. 10.5194/acp-24-6359-2024
- Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints J. Thanwerdas et al. 10.5194/acp-24-2129-2024
- How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions? J. Thanwerdas et al. 10.5194/acp-22-15489-2022
Latest update: 23 Nov 2024
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
We present here the Community Inversion Framework (CIF) to help rationalize development efforts...
